Search results for "Method"
showing 10 items of 13253 documents
Fake Nodes approximation for Magnetic Particle Imaging
2020
Accurately reconstructing functions with discontinuities is the key tool in many bio-imaging applications as, for instance, in Magnetic Particle Imaging (MPI). In this paper, we apply a method for scattered data interpolation, named mapped bases or Fake Nodes approach, which incorporates discontinuities via a suitable mapping function. This technique naturally mitigates the Gibbs phenomenon, as numerical evidence for reconstructing MPI images confirms.
A Novel Symmetrical Boost Modulation Method for qZS-based CHB Inverters
2020
Quasi-Z-source cascaded H-bridge (qZS-CHB) inverters are arising as an innovation in the field of the electrical conversion for PV applications. This type of converters inherit the advantages of multilevel inverters and single-stage configuration. In this context, this paper proposes a novel symmetrical boost modulation strategy for qZS CHB multilevel inverters to increase the performance in terms of voltage stresses and power quality. The novelty lies in the adoption of a different concept to generate the shoot-through states compared to the traditional methods. Simulation analysis in a grid connected application to evaluate the benefits of this boost method is performed in the MATLAB/PLEC…
Classification of reference models: a methodology and its application
2003
Classification is an important tool for perception and can be found in numerous scientific disciplines. Several application areas of classification are described in the context of information modeling. The usefulness of classification for reuse resp. selection of reference models is emphasized. A methodology to systematically create classification systems will be introduced. Furthermore, a classification system for reference models will be developed with the aid of the proposed methodology. This classification system gives a comprehensive, but abstract survey of 26 reference models found in the literature.
Publication and Coauthorship Networks of Hannu Oja
2015
In this paper we review Hannu Oja’s publications and form coauthor networks based on them. Applying community detection methods to the network formed by all of Hannu’s publications shows that his coauthors can be classified into 13 clusters, where two large clusters refer to his methodological research. The network concerning this methodological work is then extended to cover all statistical publications written by Hannu’s coauthors. The analysis of the extended network shows that Hannu’s coauthors do not form a closed community, but Hannu is involved in many different fields of statistics.
Exudates as Landmarks Identified through FCM Clustering in Retinal Images
2020
The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient&rsquo
Correspondences and Contrasts in Foreign Language Pedagogy and Translation Studies
2013
Correspondences and contrasts in foreign language pedagogy.- Correspondences and contrasts in translation studies.
A Comparison of Algorithms for Path Planning of Industrial Robots
2008
In this paper, the path planning problem for industrial robots in environ- ments with obstacles has been solved using four algorithms that implement different methodologies. Our objective is to analyze the characteristics of these algorithms. Consequently, the results (solutions) obtained with each of them are compared through the analysis of three operational parameters that are relevant to determine the qualities of the solutions. These parameters are: the computational time, the distance travelled by the robot and the number of generated configurations. One of the algorithms can be catalogued as indirect and the other three are variations of a direct method. The four algorithms have been…
Least-squares community extraction in feature-rich networks using similarity data
2021
We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may …
MetNet: A two-level approach to reconstructing and comparing metabolic networks
2021
Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways a…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …